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Record W3211158218 · doi:10.14740/wjon1406

Metastatic Clear Cell Renal Cell Carcinoma: The Great Pretender and the Great Dilemma

2021· article· en· W3211158218 on OpenAlexvenueno aff
Umberto Maestroni, Donatello Gasparro, Francesco Ziglioli, Giulio Guarino, Davide Campobasso

Bibliographic record

VenueWorld Journal of Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRenal cell carcinomaNephrectomySpinal cord compressionPresentation (obstetrics)Radiation therapyAbdominal painRadiosurgeryBack painSurgeryRadiologyNivolumabSpinal cordCancerKidneyImmunotherapyOncologyInternal medicinePathology

Abstract

fetched live from OpenAlex

Metastatic renal cell carcinoma (mRCC) may present with a wide range of clinical pictures. Reportedly, paraneoplastic syndromes are the first sign in 20% of cases and only 15% of cases show the classic triad (flank pain, gross hematuria, and palpable abdominal mass) at presentation. The remaining cases present with signs and symptoms related to the site of distant metastases. These data may explain the reason why about 20-30% of patients are metastatic at presentation. We report the case of a 63-year-old woman who came to our attention for lower back pain. After imaging studies, we detected a left kidney mass of 86 × 61 × 79 mm, multiple right pulmonary nodules and six bone lesions. She underwent left radical nephrectomy. After 1 month, she developed signs of spinal cord compression with neurological deficits and she underwent emergency spinal decompression. In order to allow complete motor recovery, the subsequent stereotactic body radiation therapy was not performed, and she is currently taking combination immunotherapy regimens. Management of mRCC is in a continuous evolution due to availability of new target therapies and the possibility of a multimodal approach with surgical, focal and radiotherapy treatments. However, the ideal treatment algorithm is yet to come. This is why mRCC diagnosis and management are still challenging for the clinicians.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.290
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2021
Admission routes1
Has abstractyes

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